Quantum-connectivity indices are topographic descriptors combining quantum-chemical and topological information. They are used to describe the water solubility of a noncongeneric data set of organic compounds. A QSPR model is obtained with two quantum-connectivity indices that accounts for more than 90% of the variance in the water solubility of these chemicals. This model is compared to other five QSPR models using constitutional, electrostatic, geometric, quantum-chemical, and topological descriptors calculated by CODESSA. None of these models accounts for more than 85% of the variance in water solubility of the compounds in this data set. The QSPR model obtained with quantum-connectivity indices is also better than that generated from the general pool of 508 CODESSA indices. Models with up to five variables were explored and compared with the model obtained here. It is shown that quantum-connectivity indices contain more structural information than other classes of descriptors at least for describing the water solubility of these 53 chemicals. Structural interpretation of the QSPR model developed as well as the role of the quantum-connectivity indices included in it are also analyzed.
- Molecular descriptors
- Water solubility
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computational Mathematics